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Personas

Data-Driven Search Personas for SEO and SEM

Why You Need Data-Driven Search Personas

Leveraging search via search engine optimization (SEO) or keyword advertising (SEM) is all about connecting your business with potential customers that have expressed an interest in your product, service, or content via keywords in a search.

Therefore, these searches are direct and explicit expressions by real people. As such, targeted keyword search is a very effective pull marketing strategy.

Data-Driven Search Personas Can Enhance Your SEO Performance
Data-Driven Search Personas Can Enhance Your SEO Performance

Defining Data-Driven Search Personas

For enhanced understanding that these are real people, consider creating data-driven search personas.

Data-driven search personas are humanized representations of people based on keyword search analytics.

You can create data-driven search personas from a combination of site analytics data and keyterms from, for example, your search console platform, along with 2nd and 3rd party data to round out the persona profile. Data-driven search personas can increase your SEO marketing campaigns’ effectiveness, so it is well worth it to create them!

Benefits of Data-driven Search Personas

Search personas permit you to more easily focus attention across the entire organization on how a single persona searches for your content, product, or service. This focus on a common view of search personas enables streamlining the keyword SEO process and creating online content tailored to and compelling to a single persona. Data-driven search personas can help you better understand these potential customers by presenting a humanized representation to the people behind the search.

Data-driven search personas are especially effective in aiding your organization by:

  • Keeping top of mind that real people are doing searches – Data-driven search personas present a consistent customer representation across all of your organization’s divisions, including those that don’t access search data or want to access it. For example, a persona consistently comes to the site via certain keyterms but has a high bounce rate. You can share the data-driven search persona with the usability team, the content creation team, and the product team for a common enterprise-level analysis of what might be causing the high bounce rate for this potential customer segment.
  • Creating specific, valuable online content for these potential customers – Data-driven search personas give a clear focus, from C-level guidance to the individual copy editor. This content may be an end in itself if you are a media company, or the content might be tailored to provide information about your company if your business goal is branding. The content can provide information about your product or service if your business goal is conversions.
  • Improving the quality of your search leads – Using the integrated data sources, the humanized representation allows for genuine understanding and reflection on the motivation behind the search traffic. For example, if the search is a navigational one, the persona already knows your company, product, or service. Using algorithmic techniques, you can infer additional interest and open up the possibility of upselling or cross-selling.

Steps for Creating Data-Driven Search Personas

You can use a persona analytics system, like APG, to create data-driven search personas.

You can also do it yourself using the following Six Step Data-Driven Search Personas Creation Process:

Step 1: Create primary and secondary personas – If you have not created primary personas and secondary personas for your organization, do so first before developing search specific ones. Search personas do not replace these personas. Instead, search personas are a specific view of these primary personas and secondary personas from a SEO perspective. You use search personas for this particular SEO task.

Step 2: Compile a list of search terms and analytics – Using the data-driven approach, start with the list of searches that are pulling people to your site. If you have primary data, such as the Google Search Console, this is an excellent place to start. If you don’t have 1st party data, you can leverage 2nd and 3rd party data from many online services.

For each search, leverage the associated analytics to rank the importance of each query. For example, impressions are important for branding. Clicks are important for converts. Click-thru-rate (CTR) is important for competitive analysis.

Step 3: Cull the list of search terms and analytics – Using your primary personas and secondary personas as a guide, remove all the searches that are not related to your primary and secondary customers. For example, if you work for a retail business in Thailand that only services domestically, searches for ‘international shipping’ do not fit your primary or secondary personas.

Pro Tip – These searches that you cull might be from your anti-personas, which are people who think they are your customer but aren’t.

Once culled, you now have a list of searches and analytics aligned with your organization’s personas.

Step 4: Segment the searches – This is a crucial step for accurate and enhanced customer understanding. Rather than focusing on each individual search, segment the searches based on a perspective that you believe is most important for your business. This perspective is a strategic decision based on your organization’s goals and key performance indicators (KPIs) that your organization considers necessary.

Some common segmenting approaches for search are:

  • Theme: Group the searches by topic, which usually involves overlapping terms within each query. This grouping will result in topical clusters, and the topical classification can be done algorithmically. The final segmenting into themes generally requires human decision-making. There might (most likely will) be searches that fall into multiple topics and themes. This is fine.
  • Intent: Most searches can be classified into three general groups based on the inferred intend of the queries. These groupings are:
    • Navigational: the person wants to get to a site
    • Transactional: the person was to execute an action at some site
    • Informational: the person wants information
    • Mixed: There might be searches that fall into more than one of these intents.
  • Customer journey: There are various customer journey frameworks that you can use to segment the searches. A very workable customer journey framework is based on three phases. Search volumes decrease as you move down the funnel, but the smaller volume may be more impactful to revenue. The three stages of the customer journey are:
    • Phase 1 Top of Funnel (TOF): Research – these searches are about information gathering
    • Phase 2 Middle of Funnel (MOF): Decision – these searches are about narrowing the possible product/service choices
    • Phase 3 Bottom of Funnel (BOF): Purchase – these searches are about finding the right vendor for the purchase of the product/service

Step 5: Build base user-profiles – For this step, you aggregate the segmented searches with searcher demographics data or visitors to your website. Again, this demographic data can be 1st party data, such from the Google Analytics console, and then augmented with 2nd and 3rd party data. This linkage between search and demographic data can involve some judgments, as few searches come in with demographical data. However, prior research shows that one can infer gender, age, and a host of other demographic insights using searches alone.

Step 6: Enrich the user profiles to create complete search personas – In this final step, you enrich the user profiles to make your set of data-driven search persona. Attributes for enrichment include demographics like name, photo, education, relationship status, topics of interest derived from your search terms, and associated interests derived from 3rd party interests. The APG system enriches these profiles automatically using internal databases of meta-tagged names and meta-tagged photos, along with API access to 3rd party data services. However, you can also do them manually.

Summary of Data-Driven Search Personas

So, that is the data-driven search persona creation process!

  • Step 1: Create primary and secondary personas
  • Step 2: Compile a list of search terms and analytics
  • Step 3: Cull the list of search terms and analytics
  • Step 4: Segment the searches
  • Step 5: Build base user-profiles
  • Step 6: Enrich the user profiles to create complete search personas

However, data-driven personas will only benefit your organization’s SEO efforts if they are used! For practical use, you must educate stakeholders on the value, invest the time in the creation, and employ them within the organization’s workflow.

Here is an example of data-driven search persona for this APG blog based on persona analytics methods like those used in the APG system.

APG Data-Driven Search Persona for the APG Blog
Data-Driven Search Persona for the APG Blog

Want more about employing persona analytics?

Jansen, B. J., Salminen, J., Jung, S.G., and Guan, K. (2021). Data-Driven Personas. Synthesis Lectures on Human-Centered Informatics,1 Carroll, J. (Ed). Morgan-Claypool: San Rafael, CA., 4:1, i-317.

By Jim Jansen

Dr. Jansen is a Principal Scientist in the social computing group of the Qatar Computing Research Institute, and a professor with the College of Science and Engineering, Hamad bin Khalifa University, and an adjunct professor with the College of Information Sciences and Technology at The Pennsylvania State University. He is a graduate of West Point and has a Ph.D. in computer science from Texas A&M University, along with master degrees from Texas A&M (computer science) and Troy State (international relations). Dr. Jim Jansen served in the U.S. Army as an Infantry enlisted soldier and communication commissioned officer.